DiGS-Avatar: Single-Image Animatable 3D Human Reconstruction via UV-Space Diffusion

Single-image 3D human reconstruction often suffers fromover-smoothed textures and geometric inconsistencies. While diffusionmodels improve generative quality, their reliance on multi-view synthe-sis prior to 3D reconstruction is computationally expensive and proneto view inconsistency. We propose DiGS-Avatar, which reformulates thistask as an efficient, diffusion-based UV-latent completion task, ensur-ing 3D consistency by design. To capture accurate spatial structure, weintroduce a teacher-student framework where a multi-view teacher pro-vides geometrically aligned pseudo-ground-truth latents to supervise asingle-view diffusion student. Treating this inferred latent as a robuststructural skeleton, our method injects high-level semantic features to ac-curately recover fine textural details without disrupting spatial integrity.The refined representation is then decoded into 3D Gaussian primitives.Extensive experiments demonstrate that DiGS-Avatar achieves state-of-the-art or highly competitive visual fidelity and zero-shot generalization,while reconstructing a fully animatable 3D avatar in just 0.71 seconds.Code is available at https://github.com/KLMAV-CUC/DiGS-Avatar.